InsightVista: Unveiling visitor sentiments and trends for Terengganu state museum using text analytics
This project explores visitor experiences by conducting sentiment analysis and clustering analysis on 1,973 visitor reviews posted on Google Maps and TripAdvisor. Utilizing machine learning algorithms like Support Vector Machine (SVM) and the K-Means algorithm, the project achieved 87.66% accuracy in sentiment classification and identified three optimal clusters. Then, a detailed analysis of both results were conducted. The insights are visualized through an interactive dashboard, enabling museum management to make informed decisions. Despite data limitations, the project offers strategic recommendations for ongoing improvement, aiming to enhance visitor satisfaction and engagement at the Terengganu State Museum.
Authors
- Nor Hasnul Azirah Abdul Hamid (ORCID: https://orcid.org/0000-0002-5455-5509)
- Norulhidayah Isa
- Sarah Yusoff
- Nurul Husna Tarmizi
- Nur Iman Husna A’riff
Institutions
- Universiti Malaysia Terengganu (MY)
Publication Details
- Journal
- Environment-Behaviour Proceedings Journal
- Published
- 2025-05-31
- DOI
- https://doi.org/10.21834/e-bpj.v10isi31.6938
- Primary Topic
- Diverse Aspects of Tourism Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00